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The exceptional architecture designed by master architects is a shared treasure of humanity, which embodies their design skills and concepts not possessed by common architectural designers. To help ordinary designers improve the design quality, we propose a new artificial intelligence (AI) method for generative architectural design, which generates designs with specified styles and master architect quality through a diffusion model based on textual prompts of the design requirements. Compared to conventional methods dependent on heavy intellectual labor for innovative design and drawing, the proposed method substantially enhances the creativity and efficiency of the design process. It overcomes the problem of specified style difficulties in generating high-quality designs in traditional diffusion models. The research results indicated that: (1) the proposed method efficiently provides designers with diverse architectural designs; (2) new designs upon easily altered text prompts; (3) high scalability for designers to fine-tune it for applications in other design domains; and (4) an optimized architectural design workflow.

Neural tumors can generally be divided into central nervous system tumors and peripheral nervous tumors. Because this type of tumor is located in the nerve, even benign tumors are often difficult to remove by surgery. In addition, the majority of neural tumors are malignant, and it is particular the same for the central nervous system tumors. Even treated with the means such as chemotherapy and radiotherapy, they are also difficult to completely cure. In recent years, an increasingly number of studies have focused on the use of mRNA to treat tumors, representing an emerging gene therapy. The use of mRNA can use the expression of some functional proteins for the treatment of genetic disorders or tissue repair, and it can also be applied to immunotherapy through the expression of antigens, antibodies or receptors. Therefore, although these therapies are not fully-fledged enough, they have a broad research prospect. In addition, there are many ways to treat tumors using mRNA vaccines and exosomes carrying mRNA, which have drawn much attention. In this study, we reviewed the current research on the role of mRNA in the development, diagnosis, treatment and prognosis of neural tumors, and examine the future research prospects of mRNA in neural tumors and the opportunities and challenges that will arise in the future application of clinical treatment.

RNA methylation modifications, especially m6A mRNA modification, are known to be extensively involved in tumor development. However, the relationship between N3-methylcytidine (m3C) related genes and tumorigenesis has rarely been studied. In this research, we found that m3C-related genes were expressed at different levels and affected patients’ prognosis across multiple cancer types from The Cancer Genome Atlas and multi-omics levels. Importantly, methyltransferase-like proteins 2A (METTL2A) had a high amplification frequency (~ 7%) in patients with breast invasive carcinoma (BRCA), and its overexpression was an independent predictor of poor overall survival. Enrichment analysis of associated genes revealed that METTL2A may activate DNA synthesis and cell proliferation pathways in BRCA cells. Through drug sensitivity analysis, Trifluridine, PD407824, and Taselisib were shown to be effective drugs for METTL2A-positive BRCA patients. Overall, our research conducts a holistic view of the expression level and prognostic signature of m3C-related genes with multiple malignancies. Importantly, METTL2A has been intensely explored as a potential oncogene in BRCA, to aid the development of potential drug agents for precision therapy in breast cancer patients.

Toona sinensis (A. Juss.) Roem., which is widely distributed in China, is a homologous plant resource of medicine and food. The leaves, seeds, barks, buds and pericarps of T. sinensis can be used as medicine with traditional efficacy. Due to its extensive use in traditional medicine in the ancient world, the T. sinensis plant has significant development potential. In this review, 206 compounds, including triterpenoids (1–133), sesquiterpenoids (134–135), diterpenoids (136–142), sterols (143–147), phenols (148–167), flavonoids (168–186), phenylpropanoids (187–192) and others (193–206), are isolated from the T. sinensis plant. The mass spectrum cracking laws of representative compounds (64, 128, 129, 154–156, 175, 177, 179 and 183) are reviewed, which are conducive to the discovery of novel active substances. Modern pharmacological studies have shown that T. sinensis extracts and their compounds have antidiabetic, antidiabetic nephropathy, antioxidant, anti-inflammatory, antitumor, hepatoprotective, antiviral, antibacterial, immunopotentiation and other biological activities. The traditional uses, chemical constituents, compound cracking laws and pharmacological activities of different parts of T. sinensis are reviewed, laying the foundation for improving the development and utilization of its medicinal value.

6-Aryl-2-picolinic acid herbicides are an important subclass of auxin herbicides, characterized by their good absorption and conductivity, broad weed control spectrum, and excellent herbicidal activity against some resistant weeds. Based on previous studies from our group and the distinct characteristics of physico-chemical properties and biological activities of active skeleton structure containing fluorine atoms, this paper introduces the design and synthesis of 41 novel 4-amino-6-(5-aryl-substituted-1-pyrazolyl)-3-chloro-5-fluoro-2-picolinic acid compounds. The test of inhibiting A. thaliana roots growth showed that most of the S-series compounds exhibited superior inhibitory effects compared to picloram, with six compounds demonstrated even better inhibitory capability than the new herbicidal molecule florpyrauxifen. For example, compound S202, at a concentration of 0.5 µmol/L, exhibited a 78.4% inhibition of A. thaliana root growth, whereas florpyrauxifen showed only a 33.8% inhibition. Root growth inhibition tests on weeds showed that 28 compounds, at a concentration of 250 µM, demonstrated a greater than 80% inhibition of Brassica napus (BN) root growth. Post-emergence herbicidal activity tests showed that most compounds exhibited good inhibitory effects on broadleaf weeds, with 10 compounds achieving a 100% inhibition of the growth of Amaranthus retroflexus L (AL). These results demonstrate that some of the 4-amino-6-(5-aryl-substituted-1-pyrazolyl)-3-chloro-5-fluoro-2-picolinic acid compounds could be used as potential lead structures in the discovery of novel synthetic auxin herbicides.

Background. The Citrus aurantium- (ZhiShi, ZS-) Rhizoma Atractylodis Macrocephalae (BaiZhu, BZ) pairs are often found in herbal formulas for constipation. The volatile oils of ZS and BZ (ZBVO) have good pharmacological activity against constipation, but the mechanism for treatment of slow transit constipation (STC) remains unclear. Method. A rat model using diphenoxylate tablets was constructed to investigate if transdermal administration of ZBVO would mediate intestinal microorganisms and fecal metabolites and improve STC symptoms. The regulatory effects of ZBVO at 0.15, 0.30, and 0.60 mL kg-1 d-1 on STC rats were assessed by measuring fecal water content, intestinal propulsion rate, histopathology, expression of gastrointestinal hormones, brain and intestinal peptides, and inflammatory factors. The changes in intestinal flora of STC rats were analyzed by 16S rRNA gene sequencing. Moreover, the untargeted fecal metabolomics analysis was performed by ultraperformance liquid chromatography quadrupole time-of-flight mass spectrometer (UPLC-Q-TOF-MS) technology. Results. The results showed that ZBVO had a modulating effect on STC by increasing the fecal water content and intestinal propulsion rate. Transdermal administration of ZBVO decreased serum levels of interleukin 6 (IL-6) and tumor necrosis factor-α (TNF-α) and increased the levels of gastrin (GAS) and substance P (SP). In addition, ZBVO increased 5-hydroxytryptamine (5-HT) levels and decreased vasoactive intestinal peptide (VIP) levels in colon and hippocampus tissues. The results of intestinal microbiota showed that ZBVO improved the diversity and abundance of intestinal microbiota and changed the community composition by decreasing Romboutsia and increasing Proteobacteria, Allobaculum, and Ruminococcaceae. And the feces metabolomics found that nicotinate and nicotinamide metabolism, purine metabolism, citrate cycle (TCA cycle), pyruvate metabolism, arachidonic acid metabolism, pyrimidine metabolism, and primary bile acid biosynthesis were modulated. Conclusion. These findings suggest that ZBVO can alleviate STC symptoms by promoting intestinal peristalsis, increasing fecal water content, regulating gastrointestinal hormone level, reducing the inflammatory response, and regulating brain and intestinal peptides after transdermal administration. And structural changes in the intestinal microbiota are closely related to host metabolism and intestinal microbiota destroyed in STC modeling could be significantly improved by the ZBVO, which provides a reference for the development of aromatic drug macrohealth products.

AI-driven interior design generation offers promising applications. However, current AI-based diffusion models struggle to generate indoor layouts in pixel-level alignment with the indoor structure. This study proposes a new stable diffusion-based interior design workflow with an Interior Design Control Network (IDCN). IDCN ensures that the batch-generated creative interior designs based on an input image of an unfurnished room match the indoor structure. Generating innovative designs and rendering images directly with the proposed method eliminates the tedious creative design and drawing work in traditional design practices. The results indicate that the proposed method with the new design approach achieves nearly real-time design generation and modification and significantly enhances design creativity and efficiency. Moreover, the proposed method can be generalized to other design generation tasks, thereby promoting the transformation toward intelligent design.

Interferons (IFNs) bind to cell surface receptors and activate the expression of interferon-stimulated genes (ISGs) through intracellular signaling cascades. ISGs and their expression products have various biological functions, such as antiviral and immunomodulatory effects, and are essential effector molecules for IFN function. ISGs limit the invasion and replication of the virus in a cell-specific and region-specific manner in the central nervous system (CNS). In addition to participating in natural immunity against viral infections, studies have shown that ISGs are essential in the pathogenesis of CNS disorders such as neuroinflammation and neurodegenerative diseases. The aim of this review is to present a macroscopic overview of the characteristics of ISGs that restrict viral neural invasion and the expression of the ISGs underlying viral infection of CNS cells. Furthermore, we elucidate the characteristics of ISGs expression in neurological inflammation, neuropsychiatric disorders such as depression as well as neurodegenerative disorders, including Alzheimer’s disease (AD), Parkinson’s disease (PD), and amyotrophic lateral sclerosis (ALS). Finally, we summarize several ISGs (ISG15, IFIT2, IFITM3) that have been studied more in recent years for their antiviral infection in the CNS and their research progress in neurological diseases.

In this paper, we propose a multi-stage evolutionary framework with adaptive selection (MSEFAS) for efficiently handling constrained multi-objective optimization problems (CMOPs). MSEFAS has two stages of optimization in its early phase of evolutionary search: one stage that encourages promising infeasible solutions to approach the feasible region and increases diversity, and the other stage that enables the population to span large infeasible regions and accelerates convergence. To adaptively determine the execution order of these two stages in the early process, MSEFAS treats the optimization stage with higher validity of selected solutions as the first stage and the other as the second one. In addition, at the late phase of evolutionary search, MSEFAS introduces a third stage to efficiently handle the various characteristics of CMOPs by considering the relationship between the constrained Pareto fronts (CPF) and unconstrained Pareto fronts. We compare the proposed framework with eleven state-of-the-art constrained multi-objective evolutionary algorithms on 56 benchmark CMOPs. Our results demonstrate the effectiveness of the proposed framework in handling a wide range of CMOPs, showcasing its potential for solving complex optimization problems.

Edge computing and the Internet of Things (IOT) provide the technological basis for the development of intelligent manufacturing nowadays. In order to support the intelligent interconnection and application of all kinds of equipment in the industrial field, edge computing should be equipped close to or embedded in all kinds of equipment nodes in the industrial wireless network. Therefore, it is meaningful to investigate the wireless network design of the Industrial Internet of Things. Low power wireless sensor devices are widely used in the Industrial Internet of Things (IIoT), which are sensitive to electromagnetic noise. The electromagnetic noises in industrial scenarios are significantly different from the conventional assumed white noise. In this paper, the measurement results of electromagnetic noises at three different test positions are given in an automobile factory. The spectrum occupancy of the factory wireless environment in the 300 MHz–3 GHz band was obtained by frequency domain measurement. In the time domain measurement, four statistical parameters of the three bands of 315 MHz, 433 MHz, and 916 MHz were measured, and the electromagnetic noise distributions in different plant areas and different frequency bands were analyzed. According to the measurement results, the time-varying characteristics of electromagnetic noise can be characterized by continuous hidden Markov models (CHMM). These results are informative to the design and optimization for the edge computing networks for IIoT.